{"id":4006,"date":"2020-12-01T02:07:54","date_gmt":"2020-12-01T02:07:54","guid":{"rendered":"https:\/\/techclot.com\/index.php\/2020\/12\/01\/qa-increasingly-benefits-from-ai-and-machine-learning\/"},"modified":"2020-12-01T02:07:54","modified_gmt":"2020-12-01T02:07:54","slug":"qa-increasingly-benefits-from-ai-and-machine-learning","status":"publish","type":"post","link":"https:\/\/techclot.com\/index.php\/2020\/12\/01\/qa-increasingly-benefits-from-ai-and-machine-learning\/","title":{"rendered":"QA Increasingly Benefits from AI and Machine Learning"},"content":{"rendered":"

QA Increasingly Benefits from AI and Machine Learning<\/a><\/p>\n

<\/p>\n

While the human element will still exist, incorporating AI\/ML will improve the QA testing within an organization.<\/p>\n

<\/strong><\/p>\n

The needle in quality assurance (QA) testing is moving in the direction of increased use of artificial intelligence (AI) and machine learning (ML). However, the integration of AI\/ML in the testing process is not across the board. The adoption of advanced technologies still tends to be skewed towards large companies<\/a>.<\/p>\n

Some companies have held back, waiting to see if AI met the initial hype as being a disruptor in various industries. However, the growing consensus<\/a> is that the use of AI benefits the organizations that have implemented it and improves efficiencies.<\/p>\n

Small- and
\nmid-sized could benefit from testing software using AI\/ML to meet some of the
\nchallenges faced by QA teams. While AI and ML are not substitutes for human
\ntesting, they can be a supplement to the testing methodology. <\/p>\n

See also:<\/strong> Real-time Applications and Business Transformation<\/a><\/p>\n

The
\nEnd-Goal of Software Testing<\/strong><\/h2>\n

As development is completed and moves to the testing stage of the system development life cycle, QA teams must prove that end-users can use the application as intended and without issue. Part of end-to-end (E2E) testing<\/a> includes identifying the following:<\/p>\n

    \n
  1. What is the scope of testing?<\/li>\n
  2. What bugs need to be targeted?<\/li>\n
  3. What are user behaviors likely to occur?<\/li>\n
  4. How should test cases be designed?<\/li>\n<\/ol>\n

    E2E testing
    \nplans should incorporate all of these to improve deployment success. Even while
    \nfacing time constraints and ever-changing requirements, testing cycles are
    \nincreasingly quick and short. Yet, they still demand high quality in order to
    \nmeet end-user needs.<\/p>\n

    Let\u2019s look at some of the specific ways AI and ML can streamline the testing process<\/a> while also making it more robust.<\/p>\n

    1.
    \nSave Time in Testing<\/strong><\/h3>\n

    AI in software
    \ntesting reduces the time spent on manually testing. Teams are then able to
    \napply their efforts to more complex tasks that require human interpretation.<\/p>\n

    Developers and
    \nQA staff will need to apply less effort in designing, prioritizing, writing,
    \nand maintaining E2E tests. This will expedite timelines for delivery and free
    \nup resources to work on developing new products rather than testing a new
    \nrelease.<\/p>\n

    2.
    \nImproved Regression Testing<\/strong><\/h3>\n

    With more rapid
    \ndeployment, there is an increased need for regression testing, to the point
    \nwhere humans cannot realistically keep up. Companies can use AI for some of the
    \nmore tedious regression testing tasks, where ML can be used to generate test
    \nscripts.<\/p>\n

    In the example
    \nof a UI change, AI\/ML can be used to scan for color, shape, size, or overlap.
    \nWhere these would otherwise be manual tests, AI can be used for validation of
    \nthe changes that a QA tester may miss.<\/p>\n

    3.
    \nSelecting the Appropriate Tests<\/strong><\/h3>\n

    When
    \nintroducing a change, how many tests are needed to pass QA and validate that
    \nthere are no issues? Leveraging ML can determine how many tests to run based on
    \ncode changes and the outcomes of past changes and tests.<\/p>\n

    ML can also
    \nselect the appropriate tests to run by identifying the particular subset of
    \nscenarios affected and the likelihood of failure. This creates more targeted
    \ntesting.<\/p>\n

    4.
    \nTesting Process Automation<\/strong><\/h3>\n

    With changes
    \nthat may impact a large number of fields, AI\/ML automate the validation of
    \nthese fields. For example, a scenario might be \u201cEvery field that is a
    \npercentage should display two decimals.\u201d Rather than manually checking
    \neach field, this can be automated.<\/p>\n

    ML can adapt to
    \nminor code changes so that the code can self-correct or \u201cself-heal\u201d
    \nover time. This is something that could otherwise take hours for a human to fix
    \nand re-test.<\/p>\n

    5.
    \nTesting Consistency<\/strong><\/h3>\n

    While QA
    \ntesters are good at finding and addressing complex problems and proving out
    \ntest scenarios, they are still human. Errors can occur in testing, especially
    \nfrom burnout syndrome of completing tedious processing. AI is not affected by
    \nthe number of repeat tests and therefore yields more accurate and reliable
    \nresults.<\/p>\n

    Software
    \ndevelopment teams are also ultimately composed of people, and therefore
    \npersonalities. Friction can occur between developers and QA analysts, particularly
    \nunder time constraints or the outcomes found during testing. AI\/ML can remove
    \nthose human interactions that may cause holdups in the testing process by
    \nproviding objective results.<\/p>\n

    6.
    \nDetermining the Root Causes for Failures<\/strong><\/h3>\n

    Often when a
    \nfailure occurs during testing, the QA tester or developer will need to
    \ndetermine the root cause. This can include parsing out the code to determine
    \nthe exact point of failure and resolving it from there.<\/p>\n

    In place of
    \ngoing through thousands of lines of codes, AI will be able to sort through the
    \nlog files, scan the codes, and detect errors within seconds. This saves hours
    \nof time and allows the developer to dive into the specific part of the code to
    \nfix the problem.<\/p>\n

    Incorporating
    \nAI\/ML Into Testing Software<\/strong><\/h2>\n

    While the human
    \nelement will still exist, introducing testing software that incorporates AI\/ML
    \nwill overall improve the QA testing within an organization. Equally as
    \nimportant as knowing when to use AI and ML is knowing when not to use it.
    \nSpecific scenario testing or applying human logic in a scenario to verify the
    \noutcome are not well suited for AI and ML.<\/p>\n

    But for
    \nunderstanding user behavior, gathering data analytics will build the
    \nappropriate test cases. This information identifies the failures that are most
    \nlikely to occur, which makes for better testing models.<\/p>\n

    AI\/ML can also specify patterns over time, build test environments, and stabilize test scripts. All of these allow the organization to spend more time developing new product and less time testing.<\/p>\n<\/p>\n

    Published at Mon, 30 Nov 2020 23:48:45 +0000<\/p>\n

    QA Increasingly Benefits from AI and Machine Learning<\/a><\/p>\n

    <\/p>\n

    While the human element will still exist, incorporating AI\/ML will improve the QA testing within an organization.<\/p>\n

    <\/strong><\/p>\n

    The needle in quality assurance (QA) testing is moving in the direction of increased use of artificial intelligence (AI) and machine learning (ML). However, the integration of AI\/ML in the testing process is not across the board. The adoption of advanced technologies still tends to be skewed towards large companies<\/a>.<\/p>\n

    Some companies have held back, waiting to see if AI met the initial hype as being a disruptor in various industries. However, the growing consensus<\/a> is that the use of AI benefits the organizations that have implemented it and improves efficiencies.<\/p>\n

    Small- and
    \nmid-sized could benefit from testing software using AI\/ML to meet some of the
    \nchallenges faced by QA teams. While AI and ML are not substitutes for human
    \ntesting, they can be a supplement to the testing methodology. <\/p>\n

    See also:<\/strong> Real-time Applications and Business Transformation<\/a><\/p>\n

    The
    \nEnd-Goal of Software Testing<\/strong><\/h2>\n

    As development is completed and moves to the testing stage of the system development life cycle, QA teams must prove that end-users can use the application as intended and without issue. Part of end-to-end (E2E) testing<\/a> includes identifying the following:<\/p>\n

      \n
    1. What is the scope of testing?<\/li>\n
    2. What bugs need to be targeted?<\/li>\n
    3. What are user behaviors likely to occur?<\/li>\n
    4. How should test cases be designed?<\/li>\n<\/ol>\n

      E2E testing
      \nplans should incorporate all of these to improve deployment success. Even while
      \nfacing time constraints and ever-changing requirements, testing cycles are
      \nincreasingly quick and short. Yet, they still demand high quality in order to
      \nmeet end-user needs.<\/p>\n

      Let\u2019s look at some of the specific ways AI and ML can streamline the testing process<\/a> while also making it more robust.<\/p>\n

      1.
      \nSave Time in Testing<\/strong><\/h3>\n

      AI in software
      \ntesting reduces the time spent on manually testing. Teams are then able to
      \napply their efforts to more complex tasks that require human interpretation.<\/p>\n

      Developers and
      \nQA staff will need to apply less effort in designing, prioritizing, writing,
      \nand maintaining E2E tests. This will expedite timelines for delivery and free
      \nup resources to work on developing new products rather than testing a new
      \nrelease.<\/p>\n

      2.
      \nImproved Regression Testing<\/strong><\/h3>\n

      With more rapid
      \ndeployment, there is an increased need for regression testing, to the point
      \nwhere humans cannot realistically keep up. Companies can use AI for some of the
      \nmore tedious regression testing tasks, where ML can be used to generate test
      \nscripts.<\/p>\n

      In the example
      \nof a UI change, AI\/ML can be used to scan for color, shape, size, or overlap.
      \nWhere these would otherwise be manual tests, AI can be used for validation of
      \nthe changes that a QA tester may miss.<\/p>\n

      3.
      \nSelecting the Appropriate Tests<\/strong><\/h3>\n

      When
      \nintroducing a change, how many tests are needed to pass QA and validate that
      \nthere are no issues? Leveraging ML can determine how many tests to run based on
      \ncode changes and the outcomes of past changes and tests.<\/p>\n

      ML can also
      \nselect the appropriate tests to run by identifying the particular subset of
      \nscenarios affected and the likelihood of failure. This creates more targeted
      \ntesting.<\/p>\n

      4.
      \nTesting Process Automation<\/strong><\/h3>\n

      With changes
      \nthat may impact a large number of fields, AI\/ML automate the validation of
      \nthese fields. For example, a scenario might be \u201cEvery field that is a
      \npercentage should display two decimals.\u201d Rather than manually checking
      \neach field, this can be automated.<\/p>\n

      ML can adapt to
      \nminor code changes so that the code can self-correct or \u201cself-heal\u201d
      \nover time. This is something that could otherwise take hours for a human to fix
      \nand re-test.<\/p>\n

      5.
      \nTesting Consistency<\/strong><\/h3>\n

      While QA
      \ntesters are good at finding and addressing complex problems and proving out
      \ntest scenarios, they are still human. Errors can occur in testing, especially
      \nfrom burnout syndrome of completing tedious processing. AI is not affected by
      \nthe number of repeat tests and therefore yields more accurate and reliable
      \nresults.<\/p>\n

      Software
      \ndevelopment teams are also ultimately composed of people, and therefore
      \npersonalities. Friction can occur between developers and QA analysts, particularly
      \nunder time constraints or the outcomes found during testing. AI\/ML can remove
      \nthose human interactions that may cause holdups in the testing process by
      \nproviding objective results.<\/p>\n

      6.
      \nDetermining the Root Causes for Failures<\/strong><\/h3>\n

      Often when a
      \nfailure occurs during testing, the QA tester or developer will need to
      \ndetermine the root cause. This can include parsing out the code to determine
      \nthe exact point of failure and resolving it from there.<\/p>\n

      In place of
      \ngoing through thousands of lines of codes, AI will be able to sort through the
      \nlog files, scan the codes, and detect errors within seconds. This saves hours
      \nof time and allows the developer to dive into the specific part of the code to
      \nfix the problem.<\/p>\n

      Incorporating
      \nAI\/ML Into Testing Software<\/strong><\/h2>\n

      While the human
      \nelement will still exist, introducing testing software that incorporates AI\/ML
      \nwill overall improve the QA testing within an organization. Equally as
      \nimportant as knowing when to use AI and ML is knowing when not to use it.
      \nSpecific scenario testing or applying human logic in a scenario to verify the
      \noutcome are not well suited for AI and ML.<\/p>\n

      But for
      \nunderstanding user behavior, gathering data analytics will build the
      \nappropriate test cases. This information identifies the failures that are most
      \nlikely to occur, which makes for better testing models.<\/p>\n

      AI\/ML can also specify patterns over time, build test environments, and stabilize test scripts. All of these allow the organization to spend more time developing new product and less time testing.<\/p>\n<\/p>\n

      Published at Mon, 30 Nov 2020 23:48:45 +0000<\/p>\n","protected":false},"excerpt":{"rendered":"

      QA Increasingly Benefits from AI and Machine Learning While the human element will still exist,…<\/p>\n","protected":false},"author":3,"featured_media":4005,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[3],"tags":[],"class_list":["post-4006","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence"],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/techclot.com\/wp-content\/uploads\/2020\/12\/fBncUT.jpg?fit=300%2C212&ssl=1","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/p3orZX-12C","jetpack-related-posts":[],"_links":{"self":[{"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/posts\/4006","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/comments?post=4006"}],"version-history":[{"count":0,"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/posts\/4006\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/media\/4005"}],"wp:attachment":[{"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/media?parent=4006"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/categories?post=4006"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/tags?post=4006"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}